26 research outputs found
Attacks on self-driving cars and their countermeasures : a survey
Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. 漏 2013 IEEE
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Reliable localization methods for intelligent vehicles based on environment perception
Menci贸n Internacional en el t铆tulo de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport
Systems (ITS) as a potential future of transportation. Today, thanks to all the
technological advances in recent years, the feasibility of such systems is no longer a
question. Some of these autonomous driving technologies are already sharing our
roads, and even commercial vehicles are including more Advanced Driver-Assistance
Systems (ADAS) over the years. As a result, transportation is becoming more efficient
and the roads are considerably safer.
One of the fundamental pillars of an autonomous system is self-localization. An
accurate and reliable estimation of the vehicle鈥檚 pose in the world is essential to
navigation. Within the context of outdoor vehicles, the Global Navigation Satellite
System (GNSS) is the predominant localization system. However, these systems are
far from perfect, and their performance is degraded in environments with limited
satellite visibility. Additionally, their dependence on the environment can make them
unreliable if it were to change.
Accordingly, the goal of this thesis is to exploit the perception of the environment
to enhance localization systems in intelligent vehicles, with special attention to
their reliability. To this end, this thesis presents several contributions: First, a study
on exploiting 3D semantic information in LiDAR odometry is presented, providing
interesting insights regarding the contribution to the odometry output of each type
of element in the scene. The experimental results have been obtained using a public
dataset and validated on a real-world platform. Second, a method to estimate the
localization error using landmark detections is proposed, which is later on exploited
by a landmark placement optimization algorithm. This method, which has been
validated in a simulation environment, is able to determine a set of landmarks
so the localization error never exceeds a predefined limit. Finally, a cooperative
localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle
detections in order to enhance the estimation provided by GNSS systems. Multiple
experiments are carried out in different simulation environments to validate the
proposed method.En un pasado no muy lejano, los veh铆culos aut贸nomos y los Sistemas Inteligentes
del Transporte (ITS) se ve铆an como un futuro para el transporte con gran potencial.
Hoy, gracias a todos los avances tecnol贸gicos de los 煤ltimos a帽os, la viabilidad
de estos sistemas ha dejado de ser una inc贸gnita. Algunas de estas tecnolog铆as
de conducci贸n aut贸noma ya est谩n compartiendo nuestras carreteras, e incluso los
veh铆culos comerciales cada vez incluyen m谩s Sistemas Avanzados de Asistencia a la
Conducci贸n (ADAS) con el paso de los a帽os. Como resultado, el transporte es cada
vez m谩s eficiente y las carreteras son considerablemente m谩s seguras.
Uno de los pilares fundamentales de un sistema aut贸nomo es la autolocalizaci贸n.
Una estimaci贸n precisa y fiable de la posici贸n del veh铆culo en el mundo es esencial
para la navegaci贸n. En el contexto de los veh铆culos circulando en exteriores, el
Sistema Global de Navegaci贸n por Sat茅lite (GNSS) es el sistema de localizaci贸n predominante.
Sin embargo, estos sistemas est谩n lejos de ser perfectos, y su rendimiento
se degrada en entornos donde la visibilidad de los sat茅lites es limitada. Adem谩s, los
cambios en el entorno pueden provocar cambios en la estimaci贸n, lo que los hace
poco fiables en ciertas situaciones.
Por ello, el objetivo de esta tesis es utilizar la percepci贸n del entorno para mejorar
los sistemas de localizaci贸n en veh铆culos inteligentes, con una especial atenci贸n a
la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones:
En primer lugar, se presenta un estudio sobre c贸mo aprovechar la informaci贸n
sem谩ntica 3D en la odometr铆a LiDAR, generando una base de conocimiento sobre la
contribuci贸n de cada tipo de elemento del entorno a la salida de la odometr铆a. Los
resultados experimentales se han obtenido utilizando una base de datos p煤blica y se
han validado en una plataforma de conducci贸n del mundo real. En segundo lugar,
se propone un m茅todo para estimar el error de localizaci贸n utilizando detecciones
de puntos de referencia, que posteriormente es explotado por un algoritmo de
optimizaci贸n de posicionamiento de puntos de referencia. Este m茅todo, que ha
sido validado en un entorno de simulaci贸n, es capaz de determinar un conjunto de
puntos de referencia para el cual el error de localizaci贸n nunca supere un l铆mite
previamente fijado. Por 煤ltimo, se propone un algoritmo de localizaci贸n cooperativa
basado en un Filtro Gen茅tico de Part铆culas para utilizar las detecciones de veh铆culos
con el fin de mejorar la estimaci贸n proporcionada por los sistemas GNSS. El m茅todo
propuesto ha sido validado mediante m煤ltiples experimentos en diferentes entornos
de simulaci贸n.Programa de Doctorado en Ingenier铆a El茅ctrica, Electr贸nica y Autom谩tica por la Universidad Carlos III de MadridSecretario: Joshu茅 Manuel P茅rez Rastelli.- Secretario: Jorge Villagr谩 Serrano.- Vocal: Enrique David Mart铆 Mu帽o
Advances in Intelligent Vehicle Control
This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems
Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications
Menci贸n Internacional en el t铆tulo de doctorNavigation information is an essential element for the functioning of robotic platforms and
intelligent transportation systems. Among the existing technologies, Global Navigation Satellite
Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for
all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term
for referring to a constellation of satellites which transmit radio signals used primarily for
ranging information. Therefore, the successful operation and deployment of prospective
autonomous systems is subject to our capabilities to support GNSS in the provision of
robust and precise navigational estimates.
GNSS signals enable two types of ranging observations: 鈥揷ode pseudorange, which is a
measure of the time difference between the signal鈥檚 emission and reception at the satellite
and receiver, respectively, scaled by the speed of light; 鈥揷arrier phase pseudorange, which
measures the beat of the carrier signal and the number of accumulated full carrier cycles.
While code pseudoranges provides an unambiguous measure of the distance between satellites
and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets,
carrier phase measurements present a much higher precision, at the cost of being ambiguous by
an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum
potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase
observations which, in turn, lead to complicated estimation problems.
This thesis deals with the estimation theory behind the provision of carrier phase-based
precise navigation for vehicles traversing scenarios with harsh signal propagation conditions.
Contributions to such a broad topic are made in three directions. First, the ultimate positioning
performance is addressed, by proposing lower bounds on the signal processing realized at the
receiver level and for the mixed real- and integer-valued problem related to carrier phase-based
positioning. Second, multi-antenna configurations are considered for the computation of a
vehicle鈥檚 orientation, introducing a new model for the joint position and attitude estimation
problems and proposing new deterministic and recursive estimators based on Lie Theory.
Finally, the framework of robust statistics is explored to propose new solutions to code- and
carrier phase-based navigation, able to deal with outlying impulsive noises.La informaci贸n de navegaci贸n es un elemental fundamental para el funcionamiento de sistemas
de transporte inteligentes y plataformas rob贸ticas. Entre las tecnolog铆as existentes, los
Sistemas Globales de Navegaci贸n por Sat茅lite (GNSS) se han consolidado como la piedra
angular para la navegaci贸n en exteriores, dando acceso a localizaci贸n y sincronizaci贸n temporal
a una escala global, irrespectivamente de la condici贸n meteorol贸gica. GNSS es el t茅rmino
gen茅rico que define una constelaci贸n de sat茅lites que transmiten se帽ales de radio, usadas
primordinalmente para proporcionar informaci贸n de distancia. Por lo tanto, la operatibilidad y
funcionamiento de los futuros sistemas aut贸nomos pende de nuestra capacidad para explotar
GNSS y estimar soluciones de navegaci贸n robustas y precisas.
Las se帽ales GNSS permiten dos tipos de observaciones de alcance: 鈥損seudorangos de
c贸digo, que miden el tiempo transcurrido entre la emisi贸n de las se帽ales en los sat茅lites y su
acquisici贸n en la tierra por parte de un receptor; 鈥損seudorangos de fase de portadora, que
miden la fase de la onda sinusoide que portan dichas se帽ales y el n煤mero acumulado de ciclos
completos. Los pseudorangos de c贸digo proporcionan una medida inequ铆voca de la distancia
entre los sat茅lites y el receptor, con una precisi贸n de dec铆metros cuando no se tienen en
cuenta los retrasos atmosf茅ricos y los desfases del reloj. En contraposici贸n, las observaciones
de la portadora son super precisas, alcanzando el mil铆metro de exactidud, a expensas de ser
ambiguas por un n煤mero entero y desconocido de ciclos. Por ende, el alcanzar la m谩xima
precisi贸n con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo
cual implica unos problemas de estimaci贸n de elevada complejidad.
Esta tesis versa sobre la teor铆a de estimaci贸n relacionada con la provisi贸n de navegaci贸n
precisa basada en la fase de la portadora, especialmente para veh铆culos que transitan escenarios
donde las se帽ales no se propagan f谩cilmente, como es el caso de las ciudades. Para ello,
primero se aborda la m谩xima efectividad del problema de localizaci贸n, proponiendo cotas
inferiores para el procesamiento de la se帽al en el receptor y para el problema de estimaci贸n
mixto (es decir, cuando las inc贸gnitas pertenecen al espacio de n煤meros reales y enteros). En
segundo lugar, se consideran las configuraciones multiantena para el c谩lculo de la orientaci贸n de un veh铆culo, presentando un nuevo modelo para la estimaci贸n conjunta de posici贸n y
rumbo, y proponiendo estimadores deterministas y recursivos basados en la teor铆a de Lie. Por
煤ltimo, se explora el marco de la estad铆stica robusta para proporcionar nuevas soluciones de
navegaci贸n precisa, capaces de hacer frente a los ruidos at铆picos.Programa de Doctorado en Ciencia y Tecnolog铆a Inform谩tica por la Universidad Carlos III de MadridPresidente: Jos茅 Manuel Molina L贸pez.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi
The r-evolution of driving: from Connected Vehicles to Coordinated Automated Road Transport (C-ART)
Connected and automated vehicles could revolutionise road transport. New traffic management approaches may become necessary, especially in light of a potential increase in travel demand. Coordinated Automated Road Transport (C-ART) is presented as a novel approach that stakeholders may consider for an eventual full realisation of a safe and efficient mobility system.JRC.C.4-Sustainable Transpor
Robust GNSS Carrier Phase-based Position and Attitude Estimation
Navigation information is an essential element for the functioning of robotic platforms and intelligent transportation systems. Among the existing technologies, Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term for referring to a constellation of satellites which transmit radio signals used primarily for ranging information. Therefore, the successful operation and deployment of prospective autonomous systems is subject to our capabilities to support GNSS in the provision of robust and precise navigational estimates.
GNSS signals enable two types of ranging observations: --code pseudorange, which is a measure of the time difference between the signal's emission and reception at the satellite and receiver, respectively, scaled by the speed of light; --carrier phase pseudorange, which measures the beat of the carrier signal and the number of accumulated full carrier cycles. While code pseudoranges provides an unambiguous measure of the distance between satellites and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets, carrier phase measurements present a much higher precision, at the cost of being ambiguous by an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase observations which, in turn, lead to complicated estimation problems.
This thesis deals with the estimation theory behind the provision of carrier phase-based precise navigation for vehicles traversing scenarios with harsh signal propagation conditions. Contributions to such a broad topic are made in three directions. First, the ultimate positioning performance is addressed, by proposing lower bounds on the signal processing realized at the receiver level and for the mixed real- and integer-valued problem related to carrier phase-based positioning. Second, multi-antenna configurations are considered for the computation of a vehicle's orientation, introducing a new model for the joint position and attitude estimation problems and proposing new deterministic and recursive estimators based on Lie Theory. Finally, the framework of robust statistics is explored to propose new solutions to code- and carrier phase-based navigation, able to deal with outlying impulsive noises
Practical investigations in robot localization using ultra-wideband sensors
Robot navigation is rudimentary compared to the capabilities of humans and animals to move about their environments. One of the core processes of navigation is localization, the problem of answering where one is at the present time. Robot localization is the science of using various sensors to inform a robot of where it is within its environment. Ultra-wideband (UWB) radio is one such sensor technology that can return absolute position information. The algorithm to accomplish this is known as multilateration, which uses a collection of distance measurements between multiple robot tag and environment anchor pairs to calculate the tag鈥檚 position. UWB is especially suited to the task of returning precise distance measurements due to its capabilities of short duration, high amplitude pulse generation and detection. Decawave Ltd. has created an UWB integrated circuit to perform ranging and a suite of products to support this technology. Claimed and verified accuracies using this implementation are on the order of 10cm. This thesis describes various experiments carried out using Decawave technology for robot localization. The progression of the chapters starts with commercial product verification before moving into development and testing in various environments of an open-source driver package for the Robot Operating System (ROS), then the development of a novel phase difference of arrival (PDoA) sensor for three-dimensional robot localization without an UWB anchor mesh, before concluding with future research directions and commercialization potential of UWB. This thesis is designed as a compilation of all that the author has learned through primary and secondary research over the past three years of investigation. The primary contributions are:
1. A modular ROS UWB driver framework and series of ROS bags for offline experimentation with multilateration algorithms.
2. A robust ROS framework for comparing motion capture system (MoCap) ground truth vs sensor data for rigorous statistical analysis and characterization of multiple sensors.
3. Development of a novel UWB PDoA sensor array and data model to allow 3D localization of a target from a single point without the deployment of an antenna mesh